Bachelor Thesis
ANALYZING THE RELATIONSHIP BETWEEN
FOREIGN DIRECT INVESTMENT AND TOURISM IN
INDONESIA
Faculty of Economics and Business
Specialization: Economics and Finance
Academic Year 2014/2015
Trisya Novalia Wijayanti
10621733
Statement of Originality
This document is written by student Trisya Novalia Wijayanti, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
TABLE OF CONTENTS
I. INTRODUCTION ... 5
II. FDI AND TOURISM PERFORMANCE IN INDONESIA ... 7
III. LITERATURE REVIEW ... 14
IV. METHODOLOGY ... 19
V. DATA ... 21
V. I. Foreign Direct Investment ... 21
V. II. Number of Tourists ... 21
V. III. Labor Wage ... 21
V. IV. Debt-to-GDP ... 22
V. V. GDP ... 22
V. VI. GDP growth ... 23
V. VII. Exchange Rate ... 23
VI. RESULT ... 24 Model 1 Result ... 24 Model 2 Result ... 26 VII. CONCLUSION ... 28 VIII. APPENDIX ... 29 Appendix 1 ... 29 Appendix 2 ... 31 Appendix 3 ... 33 IX. BIBLIOGRAPHY ... 34
This thesis is dedicated to my beloved parents, Bambang Tri S. Soepandji and Yana Bambang, and my brother Audi Rafisky Soepandji. Thank you for the never-ending love,
encouragement, supports and prays of days and nights until what I have become now. I thank God for every blessings upon my life and for being there when nobody else was.
Also a dedication goes to my supervisor, Stephanie Chan, thank you for the guidance and for being very helpful during the progress of this thesis.
Thank you to Mr. Tjahya Kurnia and Ms. Ati Dewayani who have played a crucial role throughout my studies and guided me with precious ideas, and also make me able to achieve
all the successful things I’ve ever had in my life. The last but not least, thank you to all my friends in the Netherlands, and all my friends in Indonesia especially in University of
Indonesia, which I could not mention the name one by one.
Without them, the completion of this work would not have been possible.
Trisya Novalia Wijayanti
Amsterdam, July 2015
I.
INTRODUCTION
Generally speaking, the main purpose of FDI is to enlarge the operation market of a business and to make the business cost as effective as possible. According to the definition, foreign direct investment is an investment in the host country that is conducted by a foreign firm. Traditionally, foreign direct investment is divided into three categories; vertical FDI, horizontal FDI, and conglomerate FDI. Vertical FDI is about a foreign direct investment, which has a different stage of business between home and the host country. For instance, a car manufacturer based on the United States has to produce the wheel in Japan. Horizontal FDI is unlike the former, where it has the same field of business between the home and the host country. An example for this is the car manufacturer based on the United States above is assembling the car both in the U.S and Japan. Lastly, a conglomerate FDI is an unusual investment, which the business field is entirely different from where it is origin on the home country and what the investment is going to be in the host country.
Currently, FDI in the tourism industry is getting conducive, with hotel as the most obvious example and restaurants as well. One of the reasons of conducting an FDI in tourism industry is because tourism has experienced continued growth and become one of the fastest growing economic sectors in the world (UNWTO). Hence, this is believed to produce significant profits for the investors. According to UNCTAD (2010), some factors play a role as the reasons why the investors invest abroad. First, the ownership-advantage that the foreign business has an ability to compete with the local competitors of the host country; second, the strategic location of host country that the investors see an opportunity as a good investment location; and the last is an ability to directly control the business in the host country instead of hiring local firm in the home country to take over the controlling management. A country’s confidence about the quality of how it could develop its tourism industry in providing facilities for tourists (i.e. hotel) influences the attractiveness of the tourist coming to that country.
While the discussion of FDI in terms of tourism industry is not widely discussed by some researchers and it is currently being developed, this thesis attempts to study the
relationship between FDI and number of the tourists in Indonesia within the small and large scope. The small scope is to see whether there is a relation between FDI and number of tourists in each province in Indonesia, whereas the large scope is the relation between FDI from a certain country and number of tourists from that same country.
The structure of this thesis is as follows: section 2 will discuss about the FDI and tourism profile in Indonesia. Section 3 is a literature review that discusses the previous research paper in order to support the research questions of the thesis. Section 4 is about the methodology, data and the result obtained from conducting the research. Finally, section 5 will be the conclusion of this thesis, which then followed by the appendix and bibliography.
II. FDI AND TOURISM PERFORMANCE IN INDONESIA
Before moving to the literature review, this part will discuss about the performance of FDI in Indonesia. Most of the inward of FDI in Indonesia in the late 1980s came from the East Asian newly industrializing economies (NIEs); particularly Korea, Taipei, and China (Usman, 2009). The reason was that the relative appreciations of these countries’ currencies made them lose their relative wage competitiveness, and as a result, they moved their production facilities to countries with lower labor cost and where export was supported, such as Indonesia.
During the Asian crisis of 1997 – 1998, the economic and political turmoil brought the Indonesian economy, including the investment incentives, below the normal level of an average of 8% of GDP growth (Setiawan) and 6.8% FDI inflow in pre-crisis period (Asian Regional Integration Center). Over the period 1985 – 1997, the Indonesian economy grew at an annual rate of nearly 8%, that was one of the fastest and most ever achieved among developing countries. As indicated by figure 1, the annual growth of GDP rate in Indonesia reached approximately negative 13% in between 1997 and 1998.
Figure 1. Indonesia, GDP growth rate 1989 – 2003. Source: World Bank
But after the reformation era where the period of a democracy with open and liberal politics (Indonesia Investments), it led to a recovery of Indonesia’s economic condition in 1999 as well as the escalation of foreign direct investment inflows. As one of the largest economy in the Southeast Asia, Indonesia’s economy grew by 5.7% in 2013, with “The World’s Most Stable Economy in The Last Five Years”, according to The Economist
(Indonesia Investment Coordinating Board). On 23rd August 2013, the government made
some policies aimed at raising the investment incentives to all investors, which includes:
1. Streamlining investment licensing
• Cutting barriers, particularly in licensing procedures.
2. Revising the “negative investment list”
• To make the investment law more investor-friendly.
• The new updated list of sectors open for FDI will be announced soon in within the year.
3. More tax incentives
• Tax dispensation to labor-intensive industries: textile, apparel, footwear, furniture, and toys industries.
• Additional tax deduction to firms with at least 30 percent export-oriented products.
Figure 2. Progress of Investment Realization of FDI in Indonesia, 2011 – 2013 Source: The Global Economy, The World Bank
Regarding this, it is expected that FDI flows will show a significant increase in Indonesia in the near future. Figure 2 shows an increasing trend of FDI in Indonesia for the period 2004 - 2013. Moreover, according to the survey from 164 transnational companies (TNCs) for 2014 - 2016 conducted by (UNCTAD, 2014), Indonesia is one of the top host countries of investment implementation. This is supported by the graph below.
Figure 3. TNC’s top perspective host economies, 2014 – 2016 Source: UNCTAD survey, 2014.
Indonesia is one of the developing countries with lower middle-income level that had a total population of 249.9 million with total GDP of 868.3 billion U.S dollars in 2013 (World Bank). The country has a tropical climate, many provinces, appealing places and landscapes, and various cultures. These may cause Indonesia to become one of the tourism destinations. Recently, tourism sector has witnessed the prominent increase all over the world (Tiwari, 2011). According to the Indonesian Bureau of Statistics, the number of tourist coming to Indonesia is continuously increasing. Regarding that Indonesia is concerned with its growing economic growth as well as the development, tourism is foreseen as one of the tools in altering the economic growth through the raising activities of the restaurant, leisure industries and the increasing demand for hotels, travel agents, airlines and other transportation services (World Travel & Tourism Council)
Indonesia was at the third position of TNC’s top host economies as can be seen from figure 3, while it is the second rank top position among Asian countries. Cited from the Indonesia investment website, Indonesia will see a massive inflow of construction hotels and condominium hotels investment and it is expected that the value of new hotel projects reaches to 38.5 trillion rupiah, or USD $3.2 billion, that is 57.3% increase relative to the last year. The primary choice of the location of new hotels is locations that attract tourists and close to education center (Indonesia Investments, 2014).
In the article made by Sugiyarto, Blake, & Sinclar (2003) Indonesia is an emerging economy that has experienced a growth in tourism industry and increasing towards trade liberalization. Prior to the economic crisis, which began in 1997, Indonesia had experienced a strong growth with the foreign arrivals, tourist spending, and investment. Indonesia’s government had effort to attract more international investment by allowing 100% foreign ownership, introducing a tax holiday, and welcoming the non-national professional workers. However, as claimed by the Ministry of Foreign Affairs of Republic of Indonesia, the investment laws also set restrictions to foreign investors. Some of these are germ plasm cultivation; forest concessions; lumbering contractors; taxi or bus transport and small-scale water transport services; print media, TV, radio, film and cinema, including distribution and exhibition; and small-scale retail trade.
In the era of globalization, tourism is expected as one of the factors having its contribution to the economic growth of Indonesia. Therefore, the development of tourism industry will be an ideal strategy to increase revenue and foreign exchange earnings (Gearing, Swart, & Var, 1973). The development of a tourism industry in the destination country is able to raise the FDI inflows of real estate, and vice versa (Al-mulali & Fereidouni, 2014)
The most famous island in Indonesia is Bali (Ministry of Tourism, Republic of Indonesia). It has been the favorite destination for both the Indonesian visitors as well as the international visitors. The majority of the foreign visitors in Bali are from Australia, which is possibly due to the near distance between Indonesia and Australia. Bali is a place where it has rich of art, culture, and beauty scenery of beaches that cause most of the foreign visitors like to visit regularly. Another popular destinations in Indonesia are Lombok and Yogyakarta. Lombok is located in the west of Bali, and it is the capital city of the province
West Nusa Tenggara. Similar to Bali, Lombok is also surrounded by fascinating coastal zone and tourism industry is the important source of the income. Contrary to Bali and Lombok, Yogyakarta is one of the old cities in Indonesia where the Javanese culture is centered and most of the buildings are Dutch architecture, as the symbol of former Dutch colonies. In addition to the center of culture, it is also popular as a center of education since there are many college students study there.
The figure 4 below is based on the reported number of foreign tourists arrival in Bali from 2008 - 2013. It is then proved that the foreign visitors coming to one of the provinces in Indonesia are continuously increasing. A foreign exchange earnings is the rich source of the growth of tourism in Bali as mentioned in an ancient paper by (McTaggart, 1980). The economic impact of tourism stated by Soest & Kooreman (1987) can be seen from the gross domestic product (GDP) and an ability to create more employment opportunities in the tourism industry. Additionally, Rodenburg (1980) mentioned the objectives of tourism for the appropriate development in Bali, in addition to the foreign exchange earnings are: 1) increased earnings, 2) increased investment, 3) increased job opportunities, 4) increased production, 5) increased entrepreneurship, 6) increased infrastructure, and 7) the minimization of adverse social and cultural effects. Besides that the growth of tourism in Bali is believed to have a stimulating effect on the local economy, the tourism itself has a potential destruction in the society (McTaggart, 1980). He said that the demands made for the provision of facilities, the demand created for local products of handicraft industry, and the vast and obvious social and economic gulf between the Balinese and the foreign visitors are factors, which are believed to cause a disruption to the social life of the community.
Figure 4. The Yearly Number of Foreign Tourists Arrival to Bali Source: Bali Tourism Board
Such the threats that are mentioned earlier, the Indonesians have embarked on a
program of tourism development intended to minimize the possibility of the worst effects1.
Thus, it is essential for the Indonesian government to seriously and carefully develop the tourism industry. Along with this, and also supported by the increasing number of tourists in Bali, the investment in terms of tourist accommodations (i.e. hotel) are perceived as a favorable opportunity.
1 On the belief that Bali should not be protected or isolated from change, but should not be
confronted with the phenomenon of tourism in a form that it cannot be smoothly digested (McTaggart, 1980).
III. LITERATURE REVIEW
Foreign direct investment is essential for the purpose of economic development, especially in developing countries such as Indonesia (Ropingi, Saud, & Melan, 2012). Determining what causes the flow of FDI to the host countries should be the matter that has to be discussed first. Several studies have conducted a research about the economic determinants of FDI. Amirahmadi & Wu (1994) stated that the economic and political factors play a crucial role in attracting the FDI. The most relevant in terms of the economic factor is the economic growth of the country and the GDP per capita. In the article made by Tiwari (2011), it stated a paper by Anwara and Nguyen (2010) about the determinants link between FDI and economic growth. These are, for example, human capital, learning by doing, exports, macroeconomic stability, and level of financial development, and public investment.
The supply of cheap labor in a country also takes into account in attracting the FDI (Shamsuddin, 1994). If the country has a comparative advantage in terms of labor, then the firms based from foreign countries consider manufacturing their products in this labor-intensive country to implement the cost effective production. Supporting this statement, firms reach the foreign markets via exporting, which then followed by FDI as the means of foreign market control and finally invest abroad to reduce the cost of producing standardized
products (UNCTAD). Ropingi, Saud, & Melan (2012) stated that the ability of Indonesia,
especially in West Java, in attracting foreign direct investment flow is determined by several factors. Some of them are populations, minimum wages, inflation rates, incentives and location variables, and the productivity of the province. Indonesia has enabled to attract FDI because of its rich natural resources and the large size of its domestic market. Debt crisis also play the crucial role, but it seems that Indonesia doesn’t have such problems in the recent decades. Moreover, Usman (2009) pointed out that since 2006, Indonesia has maintained its government debt burden. Indonesia’s government debt is situated in a competitive position amongst China, Malaysia, Korea, and Thailand.
FDI location decisions also have become some considerations for the investors. Different regions’ policies took the first position of it. Tax or subsidy benefit, supporting investment infrastructure, and low administrative requirements are the important factors in deciding whether to locate the investment (Adams, Regibeau, & Rockett, 2014). These factors should also be applied in the case of Indonesia, since it has many provinces, in which the policies are independent to each other.
A research conducted by Abul Shamsuddin (1994) based on 36 less developed countries included the growth rate of the GDP besides the per capita GDP. The results shown that the GDP is statistically significant for a determinant of FDI, while there is a statistically insignificant of the growth of the GDP. To this extent, it is dropped from the regression model. Tsai (1994) also argued there is a positive relation between FDI and the GDP. It is believed that if the economic growth of a country is increasing significantly, as indicated by a positive growth of GDP, then it is more likely that the country receives more inflows of FDI. While GDP is mainly a proxy of market size that said to be an important factor in attracting foreign investors, the exchange rate is also important (Ismail, 2009). He explained several studies made by Kohlagen (1997); Cushman (1985); Froot & Stein (1991) that the devaluation in the host country’s currency brings a reduction in local production costs in terms of foreign currency and therefore stimulates more inflows of FDI.
Tourism is believed as one of the important sources of a country’s economic growth. Hampton & Jeyacheya (2015) stated that for less developed countries like Indonesia, tourism is significantly contribute to GDP, direct employment, and government issues. Not only the economic impact, there are several benefits take into account. For instance, as cited by (UNCTAD, 2007), the main benefit that can be expected is the impact of training, the upgrading of management processes, and links to international value chain. Tourism is an activity in which capital, infrastructure, knowledge and access to global supply chains and distribution are essential (Yazdi, Salehi, & Soheilzad, 2015). In regard to this, FDI is often considered as one of the most efficient ways to exploit those necessities. FDI inflows in the tourism sector also promote the growth of tourism and consumption. Nonetheless, the significant impact of FDI on the tourism growth in developing countries clarifies the need for public intervention through implementation of various policies. This include ‘soft’ policies such as government support for trade fairs and tourism websites’ maintenance such as
cultural and heritage sites, or ‘hard’ political policies, such as the government incentives for foreign investors to bring their tourism potential or even established sources for the country’s eco-tourism. This is believed to increasing tourism arrivals and economic development in developing countries.
Many studies have carried out a research about foreign direct investment, but it is still a little discussion in the fields of FDI in tourism sector. The practices of selected transnational corporation (TNC) hotels confirm that the use of non-equity forms is more frequent than FDI (Endo, 2005). The non-equity here refers to management contract, leasing agreement, and franchise agreement or some form of marketing agreement. However, FDI in tourism in the form of hotels and restaurants increased dramatically over time, though its relative size to the global FDI remains low. This paper also explains that the determinants of FDI in tourism are no different from other industries. These include cultural/historical/geographical distance, political and/or economic risks, level of economic development, socio-economic environments, labor costs, taxation, and investment incentives.
There is also a discussion regarding the link that connects FDI and tourism industry. FDI in tourism in Caribbean case made by Velde & Nair (2006) mainly discussed about how developing countries use a service trade, through The General Agreement on Trade in Services (GATS), as the indicator to increase the amount of inflow of FDI that is focus on the tourism sector. Their research form concluded that there is a significant impact of the number of GATS commitments with the inflow of FDI. Differ from other studies that usually shown a market size of the host country has a significant effect to FDI, this research result did not show any significant result of the market size. As a result, a variable regional tourism FDI is replaced as an alternative way of market size. This variable is then result in a better measure of the market size, even though it has a low significance. Instrumental variable method is also used in this study, to see whether there are two directional effects; number of GATS commitment affects the inflow of FDI, or the other way around. The main conclusion regarding this research is that being actively involved in services negotiations, in the form of commitments with GATS, is a good way of attracting more FDI.
Another study by Al-mulali & Fereidouni (2014) studied the interaction between tourism and FDI in real estate in OECD countries. The empirical results shown no evidence for a short-run interaction, but a long-run and bi-directional causal relationship between FDI and tourism. They added that there should be an important role of policy makers to focus on tourism sector in order to attract more international tourists and recover the real estate sectors that were hit by the recent global financial crisis. This is in line with UNCTAD that the policy of host government towards FDI is one of the factors of investor in choosing the location of the FDI.
Contrarily, Dwyer & Forsyth (1994) claimed that the overall link between foreign investment and tourism is quite tenuous particularly in Australia, through the nature of the tourism product. The foreign direct investment per se appears not to have altered the types of products offered to tourists, which changes tourism flows to Australia. This is due to the Australian owners of tourism plants have much incentives, as do foreign owners to determine and to cater the visitor needs. The accommodations in Australian tourism were originally constructed and operated by domestic investors prior to sale to foreigners.
The ironic about developing countries is most of all those big companies (that providing tourism facilities) are international companies and most of those developing countries depend on international company in tourism industry (Valenca, 1999). This is supported with the article by Jenkins (1982); the scarcity of domestic funds for investment tourism and tourism expertise result in the dependence of developing countries to the developed countries’ help that have their professional skills in the tourism field. Developing a better tourism industry then should be the consideration for a better economic condition in Indonesia. There are also some contradictions regarding the tourism FDI. The potential objects to be influenced the most other than the host countries are farmers, construction, communication, electricity, water and sewerage companies, entertainment and transport, travel agents, conference, and event management (Davidson & Sahli, 2015). Moreover, the “crowd out” domestic investments, increase leakages from imports, repatriate profits, loss of equity and control of the tourism industry, and encourage inappropriate forms and scales of development.
After all the discussion of paper made by several researches above, the remaining section is to conduct the main purpose of this thesis, which is to test whether there is any
positive relation between foreign direct investment and tourism in Indonesia. This thesis aims to see whether there is a significant relation between them. The hypotheses will be as follows:
1. The number of tourists’ arrival affects the amount of FDI in Indonesia from
the origin country of the tourists.
2. The number of tourists in each province in Indonesia affects the amount of
IV. METHODOLOGY
This thesis will take a form of quantitative research with the Ordinary Least Square (OLS) regression and the secondary data provided by some institutions, as the main source of the data. Various data sources are used, namely from the Indonesian Central Bureau of Statistics, Indonesia Investment Coordinating Board, the Ministry of Tourism and Culture of Indonesia, Bank Indonesia (BI), and the Federal Reserve of St. Bank Louis. There are two research questions with the regression model also included, namely,
(1.a) & (1.b): Does the tourism from a certain country influence the foreign direct
investment of that same country in Indonesia?
(2.a) & (2.b): Does the tourism influence the foreign direct investment within each province in Indonesia?
Point a means the regression model uses GDP as one of the independent variables, and point b means the regression model uses GDPgrowth, instead of GDP, as one of the independent variables.
The regression model for both (1.a) and (2.a) can be shown as the following:
FDIit = β0 + β1 · Touristsit + β2 · Wageit + β3 · (!"#$!"#) it + β4 · GDPit + β5 · Exchrateit + εit
And the model for both (1.b) and (2.b) can be presented as,
A table below is the description of each variable:
Variable Description
FDIit (1) The number of FDI in Indonesia from a certain country in time t
(2) The number of FDI in each province in Indonesia in time t
Touristsit (1) The number of tourists in time t from each country to Indonesia
(2) The number of tourists in each province in Indonesia
Wageit The minimum labor wage in Indonesia in time t
(!"#$!"#)it The ratio of debt in proportion to Indonesia’s Gross Domestic Poduct
in time t
GDPit The Gross Domestic Product in Indonesia in every time t
GDPgrowthit The growth of Gross Domestic Product in Indonesia in every time t
Exchrateit The exchange rate of Rupiah to US dollar in time t
Table 1. Summary of variables description
The data is quarterly from the time period of 2011 until 2013. For the first research question (1), the countries that are used in the regression model are Singapore, Malaysia, The Netherlands, Japan, and South Korea. The selected provinces in Indonesia for the second research question are DKI Jakarta, West Java, East Java, Banten, and North Sumatra. While aiming at collecting a number of countries and provinces greater than five, the data was chosen due to its availability as well as its complete data set that made it possible to take a regression test. In other words, the data was collected based on a non-random sampling or selection bias, instead of a random sampling. Rather than conducting a separate OLS regression for each five countries and five provinces, this paper conducts the regression model as a quarterly pool data instead of regressing each country and each province. This is due to the fact that there are only 12 observations per country and per province. Appendix 1 shows how the pooled data is used in regression model 1, and appendix 2 will show the pooled data used in regression model 2.
V. DATA
V. I. Foreign Direct Investment
Foreign direct investment, as can be seen from the above regression model, acts as the main dependent variable. For the first research question, this variable is based on the FDI data of the 5 countries that has already conducted an investment in Indonesia, while the second research question is based on the capital investment activity of 5 provinces in Indonesia. The main data for FDI was obtained from the Indonesia Investment Coordinating Board (BKPM). Due to the data constraint, the FDI variable here is the total number of FDI in Indonesia, instead of FDI in tourism sector. The FDI for both the first and second hypothesis is measured in millions United States dollar, for the period 2011 – 2013.
V. II. Number of Tourists
The main independent variable, number of tourists, is obtained from both the Indonesia Bureau of Statistics and the Ministry of Tourism and Culture of Indonesia. The first regression model used a number of tourists from those five countries that visit Indonesia, while the second regression model used a number of tourists in five provinces that visit Indonesia. Sharma, Johri, & Chauhan (2012) analyzed the growing number of hotel and tourism industries, bringing in huge revenues in many parts of India. The booming tourism industry led to the government implementation to induce domestic and international investments. The outcome shown an expanding number of FDI inflows in tourism industry. Correspondingly, the number of tourists is expected to have a positive impact to FDI.
V. III. Labor Wage
The labor wage variable used in the model is the growth of minimum regional wage in Indonesia. It is the wage that is constant throughout the year, unless the government changes the determination of minimum wage to every region in Indonesia.
The labor wage data is obtained both from the Indonesian Bureau of Statistics (BPS) and Wage Indicator Foundation. The first hypothesis used a minimum wage in Indonesia provided by BPS, while the second hypothesis is based on the minimum wage of each 5 provinces. The original data is in Indonesia currency that is rupiah, hence it is converted to USD based on the quarterly exchange rate from 2011 to 2013. This variable is used in regard to the paper made by Ropingi, Saud, & Melan (2012), which stated that minimum wage is one of the indicators of attracting FDI to Indonesia. The coefficient of wage is expected to have a negative sign, meaning that as the country has a rising labor costs then it is unlikely that more inflows of FDI to the country, and vice versa.
V. IV. Debt-to-GDP
The data source is from the central bank of Indonesia, Bank Indonesia (BI), and this variable is a measure of Indonesia’s debt in proportion to its Gross Domestic Product. Hence, the data is measured in percentage. Maghori (2014) stated that the debt-to-GDP have a significant negative impact on the foreign direct investment in the economy, since it is in recognition of the rising indebtedness to FDI outcomes. The data here is also shown a rising percentage of debt-to-GDP from the first quarter of 2011 until the fourth quarter of 2013. As a consequence, the expected direction of this coefficient is negative.
V. V. GDP
In accordance to the paper made by Soest & Kooreman (1987), Tsai (1994), and Hampton & Jeyacheya (2015) that tourism has a significant influence to the growth of the country and also Mah & Yoon (2010) argued that the market size of Indonesia is revealed to be significant to attract FDI. Hence, this paper also applies the GDP as one of the independent variables. The quarterly data source of this variable is from the Federal Reserve of Bank St. Louis. Following the economic theory that market size is significant in attracting FDI, thus a positive sign of GDP is expected from the regression result.
V. VI. GDP growth
The quarterly GDP growth data is obtained from Bank Indonesia (BI), and as the name
suggests, the unit is in percentage. This variable is included in the regression model since Shamsuddin (1994) conducted a research of growth rate of GDP as a determinant of FDI, even though the result was statistically insignificant. On the other hand, it is believed that if the economic growth of a country is increasing, then there is more inflows of FDI. Hence, a positive coefficient of GDP growth is expected .
V. VII. Exchange Rate
The exchange rate is also believed to have a stimulating effect in attracting FDI (Ismail, 2009). Therefore, an exchange rate of the host country, or here Indonesia, is added to the independent variable of the regression model. The data was obtained from Bank Indonesia (BI) and it is quarterly exchange rate of Indonesian Rupiahs to US dollar. The expected sign of this variable is positive, since the exchange rate data shown that Indonesia has depreciated in the period 2011 – 2013. It means that is it become more attractive for foreign investors to locate their investment in Indonesia.
The significance level used for both the regression model (1) and (2) is 0.05, or 5%. Null
hypothesis is rejected if the p-value of β1 is less than 0.05, otherwise it is accepted.
VI. RESULT
Model 1 Result
The following section shows the summary results of the first regression model:
(1.a) (1.b) FDI FDI Tourists 0.00112* 0.00114* (0.000449) (0.0004499) Wage -2.964 -1.308 (11.84793) (11.35065) Debt/GDP 8.021 -38.45 (87.82154) (80.8445) GDP 0.00949 (0.0139864) GDPgrowth -378.8 (699.0792) Exchrate 0.0807 -0.0201 (0.1028918) (0.2125054) Constant -2434.6 4123.2 (3665.562) (8144.823) N 60 60 Prob > F 0.1381 0.1522 R-squared 0.1381 0.1355
Table 2. First regression result of 5 countries, 2011 – 2013 (quarterly)
Note:
1. Coefficients of each independent variable are reported in bold 2. Standard errors in parentheses
Looking at the above regression result, the main independent variable, tourists, has a positive coefficient as what was already expected both in model (1.a) and (1.b). The number of tourists in model (1.a) affects to the increase of foreign direct investment inflows, which account for 0.00112. This number is slightly lower than in model (1.b). Regardless of a relatively small number of the goodness of fit (R-squared), the p-value is still statistically significant at 5% level for tourists variable. This means that the visitors from the five countries mentioned earlier have significant impact to the FDI in Indonesia based on those
countries. For the hypothesis statement, the null hypothesis H0 is rejected and the alternative
hypothesis H1 is accepted.
The other independent variables, namely; wage, GDP, and exchange rate coincide with the sign expectation. However, it seems quite bizarre for a positive sign of the debt/GDP variable, since traditionally, the economic theory suggests that debt/GDP has a negative relationship with FDI. The positive sign of debt/GDP here might reflect that the inflows of FDI as a form of loans from foreign countries, hence Indonesia’s external debt increases. Also the same case applies to the GDPgrowth that is impossible to have such a negative sign, because a rapid economic growth creates large domestic demands and business opportunities for foreign firms (Yin, Ye, & Xu, 2014). Although there are two models here, model (1.a) is the main reference for the result, even its number of tourists is slightly lower than in model (1.b). The reason of choosing model (1.a) is mainly because the positive coefficient of GDP in model (1.a) that is more consistent, rather than a negative coefficient of GDP growth in model (1.b). The next section will show the result of the regression model of 5 provinces in Indonesia.
Model 2 Result (2.a) (2.b) FDI FDI Tourists 0.000958* 0.000960* (0.0002934) (0.0002984) Wage -10.04 -9.936 (1.912059) (1.945574) Debt/GDP 15.85 -59.80 (72.375) (73.8804) GDP 0.0158 (0.0077419) Exchrate 0.150 -0.0191 (0.0951176) (410.4173) GDPgrowth -650.7 (0.1512956) Constant -3786.8 7532.0 (2627.028) (4536.672) N 60 60 Prob > F 0.0001 0.0001 R-squared 0.3827 0.365
Table 3. Second regression model of 5 provinces, 2011 – 2013 (quarterly)
Note:
1. Coefficients of each independent variable are reported in bold 2. Standard errors in between parentheses
The results obtained from the second regression also appeared to be statistically significant at 5% for all five provinces in Indonesia in determining the FDI in those provinces and it has a positive coefficient. The coefficient of tourists in model (2.a) can be interpreted as an increase of the number of tourists in Indonesia provinces leads to an increase of 0.000958 FDI in those provinces, while it is an increase of 0.000960 FDI in model (2.b).
Similar to the previous result of the 5 countries case, all the coefficient of independent variables; tourists, wage, GDP, and exchange rate corresponds to the expectation except the debt/GDP and GDPgrowth. Again, the main reference for the regression model is the one with GDP as one of the independent variables or model (2.a), with the same reason. The result of goodness of fit here (2.a) is higher rather than the goodness of fit in the first regression (1.a). Even so, both results for tourists are statistically significant.
VII. CONCLUSION
A discussion of the relationship between foreign direct investment and tourism was not really much in the interest of the past studies and it is not so easy to determine exactly the objective of tourism that defines foreign direct investment. In this thesis, I conduct a test to know if there is a relation that the tourism has in affecting the foreign direct investment in Indonesia. There are 6 variables used in this thesis, namely FDI, tourists, wage, debt-to-GDP, debt-to-GDP, and exchange rate. The structure of the regression model of first regression model consists of pooling 5 different countries while the second regression aims at pooling 5 different provinces in Indonesia, both for quarterly time period in 2011 – 2013.
The result shows both the tourists from foreign countries and tourists in Indonesia’s provinces are significant in determining the flows of FDI to or in Indonesia, and this is in line with the study made by Sharma, Johri, & Chauhan (2012). Even though the results are all significant, it should be also noted that there is a limitation in conducting the regression test. The limitation is that a constraint data period available, so this thesis’ regression only have three years time period (2011 – 2013). Another limitation is that the unexpected sign from the debt/GDP coefficient, which contradicts to the study based on Maghori (2014).
The role of government also needed to carefully implement better policies regarding the tourism industry in Indonesia that will leads to an increasing FDI in. The conclusion in general may conveys that tourism affects FDI, but it is better to have further research due to the fact of this thesis’ limitation. Also considering that this falls into such a premature conclusion, so other analysis is expected in order to avoid some biasness and to make more accurate interpretation.
VIII. APPENDIX
Appendix 1
Country Period Time
FDI (in US$
millions) Tourists Wage (in US$) Debt/GDP (in %)
GDP (in US$ millions) Exchan ge Rate (in %) The Netherlands Q1 2011 93.300000 33638 111.0886417 28.2 210520.3749 8901 Q2 2011 611.148300 34289 115.0971947 28.4 223360.1928 8591 Q3 2011 450.000000 57782 114.7898769 27.3 231478.6222 8614 Q4 2011 200.000000 37559 110.0255925 26.4 227555.3437 8987 Q1 2012 271.654200 33387 123.2217573 26.2 231369.3212 9082 Q2 2012 170.148570 33139 120.1782646 27.1 231020.4865 9312 Q3 2012 177.248600 174815 117.7132639 27.8 227399.0424 9507 Q4 2012 347.489730 35408 116.0170019 28.7 228204.7335 9646 Q1 2013 330.512740 31267 137.2617699 28.7 234467.1635 9707 Q2 2013 267.900000 34932 135.9730585 28.9 237774.9634 9799 Q3 2013 121.636400 56403 125.0844912 29.2 226817.158 10652 Q4 2013 207.762800 38800 114.822475 30.24 215679.982 11604 Japan Q1 2011 345.200000 106020 111.0886417 28.2 210520.3749 8901 Q2 2011 350.863100 88722 115.0971947 28.4 223360.1928 8591 Q3 2011 420.000000 125303 114.7898769 27.3 231478.6222 8614 Q4 2011 400.000000 103068 110.0255925 26.4 227555.3437 8987 Q1 2012 629.543400 108624 123.2217573 26.2 231369.3212 9082 Q2 2012 500.508320 102484 120.1782646 27.1 231020.4865 9312 Q3 2012 656.691900 130478 117.7132639 27.8 227399.0424 9507 Q4 2012 670.197280 121900 116.0170019 28.7 228204.7335 9646 Q1 2013 1151.65820 0 120401 137.2617699 28.7 234467.1635 9707 Q2 2013 1154.620000 111770 135.9730585 28.9 237774.9634 9799 Q3 2013 1330.70910 0 140518 125.0844912 29.2 226817.158 10652 Q4 2013 1063.012700 124710 114.822475 30.24 215679.982 11604 South Korea Q1 2011 139.300000 78935 111.0886417 28.2 210520.3749 8901 Q2 2011 239.426400 77109 115.0971947 28.4 223360.1928 8591 Q3 2011 440.000000 81312 114.7898769 27.3 231478.6222 8614 Q4 2011 400.000000 83240 110.0255925 26.4 227555.3437 8987 Q1 2012 510.474400 89911 123.2217573 26.2 231369.3212 9082
Q2 2012 492.791210 78828 120.1782646 27.1 231020.4865 9312 Q3 2012 233.793000 81402 117.7132639 27.8 227399.0424 9507 Q4 2012 712.645490 78848 116.0170019 28.7 228204.7335 9646 Q1 2013 774.731130 88149 137.2617699 28.7 234467.1635 9707 Q2 2013 454.160000 79494 135.9730585 28.9 237774.9634 9799 Q3 2013 406.300400 91422 125.0844912 29.2 226817.158 10652 Q4 2013 570.287600 92089 114.822475 30.24 215679.982 11604 Malaysia Q1 2011 128.400000 269027 111.0886417 28.2 210520.3749 8901 Q2 2011 130.358400 299776 115.0971947 28.4 223360.1928 8591 Q3 2011 159.570000 259309 114.7898769 27.3 231478.6222 8614 Q4 2011 200.000000 345239 110.0255925 26.4 227555.3437 8987 Q1 2012 247.760800 293596 123.2217573 26.2 231369.3212 9082 Q2 2012 92.700300 334026 120.1782646 27.1 231020.4865 9312 Q3 2012 90.169600 268769 117.7132639 27.8 227399.0424 9507 Q4 2012 98.952500 372698 116.0170019 28.7 228204.7335 9646 Q1 2013 155.350000 310582 137.2617699 28.7 234467.1635 9707 Q2 2013 222.800000 350670 135.9730585 28.9 237774.9634 9799 Q3 2013 140.796700 292117 125.0844912 29.2 226817.158 10652 Q4 2013 192.316800 427317 114.822475 30.24 215679.982 11604 Singapore Q1 2011 1138.70000 0 290186 111.0886417 28.2 210520.3749 8901 Q2 2011 774.344900 356114 115.0971947 28.4 223360.1928 8591 Q3 2011 1310.000000 301766 114.7898769 27.3 231478.6222 8614 Q4 2011 1900.00000 0 376773 110.0255925 26.4 227555.3437 8987 Q1 2012 1159.21730 0 300768 123.2217573 26.2 231369.3212 9082 Q2 2012 831.600000 344088 120.1782646 27.1 231020.4865 9312 Q3 2012 1498.26720 0 284242 117.7132639 27.8 227399.0424 9507 Q4 2012 1367.26660 0 395608 116.0170019 28.7 228204.7335 9646 Q1 2013 615.989220 319992 137.2617699 28.7 234467.1635 9707 Q2 2013 1364.16000 0 361639 135.9730585 28.9 237774.9634 9799 Q3 2013 1145.51150 0 315571 125.0844912 29.2 226817.158 10652 Q4 2013 1545.13830 0 434858 114.822475 30.24 215679.982 11604
Appendix 2
Province
Time Period
FDI (in US$
millions) Tourists Wage (in US$) Debt/GDP (in %) GDP (in US$ millions) Exchange Rate (in %) Jakarta Q1 2011 850.70000 49533 79.20458375 28.2 210520.3749 8901 Q2 2011 673.37880 53988 82.06262368 28.4 223360.1928 8591 Q3 2011 1400.00000 56597 81.84351056 27.3 231478.6222 8614 Q4 2011 1900.00000 60482 78.44664515 26.4 227555.3437 8987 Q1 2012 1200.00000 50767 82.03038978 26.2 231369.3212 9082 Q2 2012 582.35496 58220 80.00429553 27.1 231020.4865 9312 Q3 2012 1200.00000 56894 78.36331124 27.8 227399.0424 9507 Q4 2012 1100.00000 63744 77.23408667 28.7 228204.7335 9646 Q1 2013 477.42210 59692 89.23972391 28.7 234467.1635 9707 Q2 2013 960.71000 140712 88.40187774 28.9 237774.9634 9799 Q3 2013 407.83680 61083 81.32275629 29.2 226817.158 10652 Q4 2013 745.20000 72236 74.65098242 30.24 215679.982 11604 North Sumatra Q1 2011 242.50000 461217 144.9275362 28.2 210520.3749 8901 Q2 2011 127.21150 485530 150.1571412 28.4 223360.1928 8591 Q3 2011 183.99000 529189 149.7562108 27.3 231478.6222 8614 Q4 2011 200.00000 528008 143.5406699 26.4 227555.3437 8987 Q1 2012 154.78060 494092 168.3549879 26.2 231369.3212 9082 Q2 2012 158.81908 534402 164.1967354 27.1 231020.4865 9312 Q3 2012 122.44960 530514 160.8288629 27.8 227399.0424 9507 Q4 2012 209.27252 566469 158.5113 28.7 228204.7335 9646 Q1 2013 175.29074 546931 226.6405687 28.7 234467.1635 9707 Q2 2013 230.84000 571780 224.5127054 28.9 237774.9634 9799 Q3 2013 291.19340 597518 206.5339842 29.2 226817.158 10652 Q4 2013 190.12630 598033 189.5897966 30.24 215679.982 11604 Banten Q1 2011 222.70000 15757 116.3352432 28.2 210520.3749 8901 Q2 2011 578.99200 55213 120.5331161 28.4 223360.1928 8591 Q3 2011 770.00000 52721 120.211284 27.3 231478.6222 8614 Q4 2011 600.00000 65262 115.2219873 26.4 227555.3437 8987 Q1 2012 555.81040 57842 132.1294869 26.2 231369.3212 9082 Q2 2012 841.97290 59651 128.8659794 27.1 231020.4865 9312 Q3 2012 418.70760 55877 126.2227832 27.8 227399.0424 9507 Q4 2012 899.77280 68464 124.403898 28.7 228204.7335 9646 Q1 2013 1109.34574 58150 141.6503554 28.7 234467.1635 9707 Q2 2013 1263.98000 64704 140.3204409 28.9 237774.9634 9799 Q3 2013 555.33180 58097 129.0837401 29.2 226817.158 10652
Q4 2013 791.57390 78348 118.4936229 30.24 215679.982 11604 West Java Q1 2011 1123.70000 450440 112.3469273 28.2 210520.3749 8901 Q2 2011 835.65960 473801 116.4008846 28.4 223360.1928 8591 Q3 2011 980.00000 519405 116.0900859 27.3 231478.6222 8614 Q4 2011 900.00000 515232 111.2718371 26.4 227555.3437 8987 Q1 2012 1098.64200 484065 114.7324378 26.2 231369.3212 9082 Q2 2012 942.12599 523395 111.8986254 27.1 231020.4865 9312 Q3 2012 1015.92770 519033 109.6034501 27.8 227399.0424 9507 Q4 2012 1154.00811 553379 108.0240514 28.7 228204.7335 9646 Q1 2013 1339.24098 533249 120.5315752 28.7 234467.1635 9707 Q2 2013 1653.90000 561134 119.3999388 28.9 237774.9634 9799 Q3 2013 2204.96090 585794 109.838528 29.2 226817.158 10652 Q4 2013 1926.77530 588038 100.8273009 30.24 215679.982 11604 East Java Q1 2011 207.00000 27805 82.23795079 28.2 210520.3749 8901 Q2 2011 54.16960 30800 85.20544756 28.4 223360.1928 8591 Q3 2011 250.87000 25831 84.97794288 27.3 231478.6222 8614 Q4 2011 800.00000 33111 81.45098476 26.4 227555.3437 8987 Q1 2012 244.77870 33874 85.88416648 26.2 231369.3212 9082 Q2 2012 949.53492 41079 83.7628866 27.1 231020.4865 9312 Q3 2012 232.21950 32726 82.04480909 27.8 227399.0424 9507 Q4 2012 872.24308 40834 80.86253369 28.7 228204.7335 9646 Q1 2013 605.00470 42980 87.56567426 28.7 234467.1635 9707 Q2 2013 812.63000 48735 86.74354526 28.9 237774.9634 9799 Q3 2013 609.91570 31391 79.79722118 29.2 226817.158 10652 Q4 2013 1368.70870 55036 73.25060324 30.24 215679.982 11604
Appendix 3
Heteroskedasticity Model
Pool Country Pool Province
(1) (2) (3) (4)
FDI FDI FDI FDI
Tourists 0.00112* 0.00114* 0.000958* 0.000960* (0.0005203) (0.0005231) (0.0003017) (0.0003089) Wage -2.964 -1.308 -10.04 -9.936 (10.97341) (11.39609) (1.683035) (1.864726) Debt/GDP 8.021 -38.45 15.85 -59.80 (74.79771) (73.79511) (70.94772) (68.65979) GDP 0.00949 0.0158 (0.0120096) (0.0069113) GDPgrowth -378.8 -650.7 (727.672) (442.6872) Exchrate 0.0807 -0.0201 0.150 -0.0191 (0.0942953) (0.2159753) (0.0882859) (0.1443698) Constant -2434.6 4123.2 -3786.8 7532.0 (3180.164) (8517.537) (2621.415) (4609.394) N 60 60 60 60 Prob > F 0.2079 0.218 0.0000 0.0002 R-squared 0.1381 0.1355 0.3827 0.365
Table 4. Regession result of both pool country and pool province,
Note:
1. Coefficients of each independent variable are reported in bold 2. Standard errors in between parentheses
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